Prediction of planarization property in copper film chemical mechanical polishing via response surface methodology and convolutional neural network

نویسندگان

چکیده

Chemical mechanical polishing (CMP) is one of the most important and effective technologies to achieve global planarization for precision machining wafer surface. The property slurry an essential index in evaluating quality copper (Cu) film CMP process. Prediction vital significance product control during Data-driven approaches predict results based on response surface methodology (RSM) neural network (NN) pursue better prediction performance were proposed this paper. In our design, all three optimization methods (RSM, NN, RSM + NN) are proved be accurate reliable, which can experimental with finer grid accuracy under condition only 17 test data points. particular, NN method higher than that other two methods, error 0.16 %. Notably, demonstrate such less time-consuming realize acquisition more desirable process parameters compositions slurries, versatility simplicity also potentially provide alternative experiment design concept a wider range applications.

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ژورنال

عنوان ژورنال: Nano select

سال: 2021

ISSN: ['2688-4011']

DOI: https://doi.org/10.1002/nano.202100028